MODELING OF CARBONIC ANHYDRASE (II) INHIBITORY ACTIVITIES OF SULPHONILAMIDE SCHIFF BASES BY ARTIFICIAL NEURAL NETWORK TRAINED WITH DIFFERENT NUMERICAL TECHNIQUES
DOI:
https://doi.org/10.22159/ijpps.2018v10i1.22775Keywords:
Sulphanilamide Schiff bases, Artificial neural network, Scaled conjugate gradient (SCG), Quasi-Newton (BFGS) and Levenberg-Marquardt (LM) algorithmAbstract
Objective: The aim of the present study was to develop robust linear and non-linear Quantitative Structure-Activity Relationship (QSAR) models for exploring the relationship between the structural features of a series of sulphanilamide Schiff bases and their CA (II) inhibition activities.
Methods: QSAR modeling of carbonic anhydrase (II) inhibiting activities of a series of sulphanilamide Schiff bases as a function of theoretically derived molecular descriptors calculated by Dragon software was established linearly by stepwise multiple linear regression (SW-MLR) method and non-linearly by artificial neural network (ANN) method, trained with different numerical techniques namely, Scaled conjugate gradient (SCG), quasi-Newton (BFGS), and Levenberg-Marquardt (LM) algorithm. SW-MLR method was also used to select descriptors from large descriptor pool. After the selection of variables, best selected linear model was validated by Y-randomization test. The applicability domain was assessed by the normalized mean Euclidean distance value for each compound. The prediction quality of proposed non-linear QSAR models was tested externally using validation and test set.
Results: The low value of R2average = 0.214 from the Y-randomization test and no significant correlation between the selected descriptors indicates that linear model is reliable, and robust. Applicability domain analysis has also revealed that the suggested model has acceptable predictability. To explore non-linear relationship between selected descriptors and the target property, ANN approach trained with three supervised algorithms (BFGS, SCG and LM) was used. Statistical comparison of the quality of models obtained using ANN method trained with above mentioned three algorithms with SW-MLR model shows that ANN with 4-3-1 architecture and trained with LM algorithm has better predictive power as indicated by low RMSEval (0.11), MAPEval (11.95) values and high R2val (0.96) value.
Conclusion: The results of this work indicated the ANN trained with fastest Levenberg-Marqardt algorithm is a promising tool for establishing non-linear relationship between selected sulphanilamide Schiffbases and their CA (II) inhibition values.
Downloads
References
Supuran CT, Clare BW. Carbonic anhydrase inhibitors, Part24. A quantitative structure-activity relationship study of positively charged sulfonamide inhibitors. Eur J Med Chem 1995; 30:687–96.
Nishimori I, Minakuchi T, Morimoto K, Sano S, Onishi S, Takeuchi H, et al. Carbonic anhydrase inhibitors: DNA cloning and inhibition studies of the alpha-carbonic anhydrase from Helicobacter pylori, a new target for developing sulfonamide and sulfamate gastric drugs. J Med Chem 2006;49:2117-26.
Supuran CT, Clare BW. Carbonic anhydrase inhibitors-part 57: Qunatum chemical QSAR of a group of 1,3,4-thiadiazole-and 1,3,4-thiadiazoline disulfonamide with carbonic anhydrase inhibitory properties. Eur J Med Chem 1999;34:41-50.
Clare BW, Supuran CT. Carbonic anhydrase inhibitors, part 41. Quantitative structure-activity correlations involving kinetic rate constantsof 20 sulfonamide inhibitors from a non-congeneric series. Eur J Med Chem 1997;32:311–9.
Clare BW, Supuran CT. Carbonic anhydrase inhibitors. Part 61. Quantum chemical QSAR of a group of benzenedisulfonamides. Eur J Med Chem 1999;34:463–74.
Khadikar PV, Clare BW, Balaban AT, Supuran CT, Agrawal VK, Singh J, et al. QSAR prediction of CAI, CAII, CAIV inhibitory activities: relative potentialof balaban and balaban type indices. Rev Roum Chem 2006;51:703–17.
Raj K, Muthukumar V. Quantitative structure-activity relationship analysis of the anticonvulsant activity of erythrinine. Asian J Pharm Clin Res 2016;9:125-9.
Katritzky AR, Lobanov VS, Karelson M. QSPR: thecorrelation and quantitative prediction of chemical and physicalproperties from structure. Chem Soc Rev 1995;24:279–87.
Roy SA. QSAR studyon the schiff bases 2, 4, 6-trichlororphenylhydrazine using freely available online 2D. Asian J Pharm Clin Res 2013;6:67-70.
Zupan J, Gasteiger J. Neural networks for chemists: An Introduction. VCH, New York; 1993.
Hassoun MH. Fundamentals of artificial neural works. MIT Press: Cambridge, MA; 1995.
Basheer A, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 2000;43:3-31.
Zupan J, Gasteiger J. Neural networks in chemistry and drug design, Wiley-VCH, Weinheim, Germany; 1999.
Bose NK, Liang P. Neural Networks, Fundamentals, McGraw-Hill, New York, NY, USA; 1996.
Melagraki G, Afantitis A, Sarimveis H, Igglessi-Marapouln O, Supuran CT. QSAR study on para-substituted aromatic-sulphonamides as carbonic anhydrase II inhibition using topological information indices. Bioorg Med Chem 2006; 14:1108-14.
Balaban AT, Basak SC, Beteringhe A, Mills D, Supuran CT. QSAR study using topological indices for inhibition of anhydrase II by sulphonamides and schiff's base. Moldivers 2004;8:401-12.
Supuran CT, Clare BW. Carbonic anhydrase inhibitors, Part 47. Quantum chemical quantitativestructure-activity relationships for a group of sulfanilamide Schiff base inhibitors of carbonic anhydrase. Eur J Med Chem 1998; 33:489-500.
Saxena A, Khadikar PV. QSAR studies on sulfanilamide schiff ’s base inhibitors of carbonic anhydrase. Acta Pharm 1999;49:171–9.
Agrawal VK, Shrivastava S, Khadikar PV, Supuran CT. Quantitative structure-activity relationshipstudies on sulfanilamide schiff bases: CA inhibitors. Bioorg Med Chem 2003;11:5353-62.
Krenkel G, Castro EA. Synthesis and structure−activity relationship of novel, highly potent metharyl and methcycloalkyl cyclooxygenase-2 (COX-2) selective inhibitors. Mole Med Chem 2003;1:13-22.
Todeschini R. Milano chemometrics and QSPR group. Available from: http://michem.disat.unimib.it/chm/staff/staff.html, http:// www.vcclab.org/lab/edragon/. [Last accessed on 20 Aug 2017].
SPSS for windows Statistical package for IBM PC, SPSS Inc.
MATLAB, Math works Inc; 2017.
Franke R. Theoratical drug design methods, Elsevier. Amsterdam; 1984. p. 184-95.
Franke R, Gruska A. Chemometric methods in molecular design. Waterbeemd H Vande. Ed. VCH, Weinheim; 1995. p. 13-163.
Snedecor GW, Cochran WG. Statistical methods, Oxford and IBH publishing Co. Pvt. Ltd., New Delhi; 1967. p. 381-418.
Golbraikh A, Tropsha A. Beware of q2!. J Mol Graph Model 2002;20:269-76.
Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R. Comparison of different approaches to define the applicability domain of QSAR models. Molecules 2012;17:4791-810.
Netzeva TI, Worth A, Aldenberg T, Benigni R, Cronin MTD, Gramatica P, et al. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report andrecommendations of ECVAM Workshop 52. Altern Lab Anim 2005;33:155–73.
Jaworska J, Nikolova Jeliazkova N, Aldenberg T. QSAR applicabilty domain estimation byprojection of the training set descriptor space: a review. Altern Lab Anim 2005;33:445–59.
Devillrs J. Neural networks in QSAR and drug design, Academic Press: London, UK; 1996.
Schneider G. Neural networks are useful tools for drug design. Neural Netw 2000;13:15-60.
ZupanJ J, GasteigerJ. Neural networks for chemists. An Introduction, VCH Publishers, Weinheim (Germany); 1993.
Afantitis A, Malagraki G, Sarimveis H, Igglessi-Markopoulou O, Kollias G. A novel QSAR model for predicting the inhibition of CXCR3 receptor by 4-N-aryl-[1, 4] diazepane ureas. Eur J Med Chem 2009;44:877-84.
Andrea TA, Kalayeh H. Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. J Med Chem 1991; 34:2824-36.
Published
How to Cite
Issue
Section
